For our work we consider the scenario where we would like a data set of faces which is private in terms of face identity, with application to face verification. The data sets commonly used for this task are composed of faces from celebrity individuals, whose identities are supposed to be in public domain. This is not always the case however, as the large number of identities needed for training face recognition algorithms requires a loose definition of celebrity, such that many of the members of the data sets object to their inclusion [1]}, [2]}. Privacy concerns become even more clear when we consider private databases such as those owned by Google and Facebook [3]}.
To build our synthetic data set we build on the StyleGAN2 architecture [4]}. StyleGAN2 is a powerful Generative Adversarial Network (GAN) that when trained on the FFHQ dataset [5]} can produce photorealistic random faces belonging to random identities. Using StyleGAN2, we propose a method which allows us to generate multiple samples from a random identity and therefore enables the creation of a face identity dataset. With part of the StyleGAN2 latent code fixed, we generate multiple images and penalise dissimilarity in facial features using the pre-trained state-of-the-art face recognition algorithm ArcFace [6]}. We evaluate our synthetic data set on the task of training a face recognition algorithm.
